the role of dedicated innovation functions for innovation process control and performance - an...

The Role of Dedicated InnovationFunctions for Innovation ProcessControl and Performance – AnEmpirical Study among Hospitals

Giso Labitzke, Sven Svoboda and Carsten Schultz

As health care systems evolve, innovation is becoming a key driver of performance in thehospital sector. However, innovation management has been adopted only sporadically inhospitals, and dedicated innovation functions remain in the developmental stage. Usingcontrol theory, this study develops a theoretical framework that links control mechanisms(proactiveness, innovation process formalization) and a dedicated innovation function toinnovation activity and innovation performance. For the empirical analysis, data were col-lected from a survey of 158 German hospitals, with information provided by general hospitalmanagement. We apply a structural equation model and control for hospital characteristicssuch as hospital size and ownership (public/non-profit/private). The empirical results showpositive effects of formal and informal control mechanisms on innovation performance and apositive effect of informal controls on innovation activity. Moreover, a dedicated innovationfunction is found to positively affect innovation activity and both formal and informal controlmechanisms. Based on our findings, we argue that hospital management might devote greaterattention to control mechanisms to increase innovation activity and performance. Furthermore,hospital management might consider the creation and empowerment of a dedicated innova-tion function to take advantage of the positive effects of such a function on informal andformal controls as well as on innovation activity.

Introduction

As a result of both rapidly changing politi-cal and economic conditions and a

dynamic technological environment, the use ofinnovation to achieve competitive advantage isgaining relevance in the health care industry.By providing complex and dynamic services,hospitals must seek to satisfy not only theircustomers (patients) but also their customers’families (Djellal & Gallouj, 2005). Hospitals,like other service organizations, must addressgrowing competition and an unstable eco-nomic environment by constantly developingand launching service innovations (Ordanini &Parasuraman, 2011). Bitner and Brown (2008)demonstrate that service innovations contrib-ute significantly to the competitiveness ofdeveloped economies.

Service innovation processes differ fromproduct innovation processes. These differ-ences are driven by service characteristics suchas intangibility and customer co-production(Fitzsimmons & Fitzsimmons, 2008). Further,service innovations are frequently not R&D-driven but may be described as ad-hoc(Gallouj & Savona, 2009). Whereas R&D-driven service innovations follow more tradi-tional new product development (NPD)process stages of idea generation, develop-ment and market introduction, ad-hoc serviceinnovations are introduced immediately afteridea generation.

Research in the field of innovation manage-ment in service organizations remains inad-equate (Page & Schirr, 2008). This studyfocuses on the German hospital market as anexample of large service providers – a market

ROLE OF DEDICATED INNOVATION FUNCTIONS 235

Volume 23 Number 3 201410.1111/caim.12068

© 2014 John Wiley & Sons Ltd

that is currently undergoing dramatic changeas a result of regulatory, technological andsocial developments, increasing the need forsuccessful innovation. Hospitals must seekmarket and clinical opportunities in the faceof internal and external pressures (Lansisalmiet al., 2006; Salge & Vera, 2009; Schultz,Zippel-Schultz & Salomo, 2012). In seekingsuch opportunities, hospitals are not merelyadopting medical and pharmaceutical pro-duct innovations; rather, process innovations,i.e., changes in the way work is carried out inorganizations (Piening, 2011), are helpinghospitals improve cost-effectiveness, provid-ing higher quality care, reducing the length ofpatient stays and increasing employee andpatient satisfaction (Shortell et al., 1995;Cheah, 2000; Douglas & Judge, 2001). Exam-ples of process innovations that have beenadopted by hospitals to improve their opera-tional efficiency are new managementapproaches such as total quality management,business process reengineering, informationtechnologies and treatment procedures(Savitz & Kaluzny, 2000; Lansisalmi et al.,2006; Piening, 2011).

Existing research focuses on both enablersof and barriers to hospital innovation (Paré,2002; Lansisalmi et al., 2006). Such researchhas examined, in particular, the support andparticipation of physicians and management(Carman et al., 1996; Weiner, Shortell &Alexander, 1997; Walston, Burns & Kimberly,2000), knowledge-based resources (Kimberly& Evanisko 1981; Walston, Bogue & Schwartz,1999), organizational structures and incentives(Douglas & Judge, 2001; Schultz, Zippel-Schultz & Salomo, 2012), business and projectplanning (Zippel-Schultz & Schultz, 2011),financial resources (Savitz & Kaluzny, 2000;Alexander et al., 2006), and organizationalculture (Shortell et al., 1995; Parker et al.,1999). In addition, the hospital literature (e.g.,Fottler, 1987; Wheeler et al., 1999; McCracken,McIlwain & Fottler, 2001; Rosenau & Linder,2003) has identified important predictors ofhospital performance. However, the hospitalliterature has seldom investigated the role ofinnovation (Salge & Vera, 2009). In this paper,we focus on the role of innovation in improv-ing hospital performance. In particular, weaim to contribute to the literature by investi-gating the role of a dedicated innovation func-tion as a vehicle for the establishment ofvarious control mechanisms and improvinginnovation performance. We thus seek toexpand knowledge of the role of innovation inservice industries and service innovation-oriented businesses.

An innovation strategy can foster improvedbusiness performance, organizational devel-

opment and competitiveness (Cooper, 1984;Wolfe, 1994). However, in addition to an inno-vation strategy and the appropriate tools,organizational support is required to benefitfrom such tools. A dedicated function consistsof a person or group of persons centrallyinvolved in the tasks of initiation, selection,coordination, control and evaluation. It iswidely viewed as an important vehicle for theaccumulation and transfer of specializedcompetences as well as an instrument thatlinks an organization’s strategic concerns withthe needs of specific innovative activities(Dyer, Kale & Singh, 2001; Kale, Dyer & Singh,2002). This study examines how the existenceand empowerment of a dedicated innovationfunction influences innovation activity andinnovation performance as well as variouscontrol mechanisms.

In addition, we examine innovation fromthe perspective of organizational controltheory (Cardinal, 2001), focusing on bothformal and informal control mechanisms. Inparticular, we focus on innovation process for-malization, as an example of a formal controlmechanism, and on proactiveness, as anexample of an informal control mechanism.Moreover, a well-structured and formalizedinnovation process has been shown to be acrucial component of successful innovationperformance (Cooper, 1990, 2008; Tatikonda &Montoya-Weiss, 2001; Kleinschmidt, deBrentani & Salomo, 2007; Schultz et al., 2013), afinding that also applies to service innovations(Drew, 1995; Song, Song & Di Benedetto, 2009).In addition to formal control mechanisms, aninnovative environment based on proactivebehaviour and a proactive learning culture areconducive to successful innovation initiativesin hospitals (Lukas et al., 2007; Khatri, Brown& Hicks, 2009). We aim to contribute to theliterature by answering the following tworesearch questions: ‘What role does the exist-ence and empowerment of a dedicated inno-vation function play in hospitals?’ and ‘Howdo different control mechanisms influence theinnovation activity and innovation perfor-mance in hospitals?’.

The paper proceeds as follows. In the nextsection, we present the theoretical backgroundof formal and informal control mechanismsand control theory. We develop our hypoth-eses accordingly. The following section pre-sents the research setting and sampleattributes as well as the constructs used asdependent, independent and control variables.We test our hypotheses, using data from 158German hospitals, combining survey informa-tion and objective information regarding hos-pital characteristics. We then report ourempirical findings in the following section,

236 CREATIVITY AND INNOVATION MANAGEMENT

Volume 23 Number 3 2014© 2014 John Wiley & Sons Ltd

after which we interpret our results anddiscuss the implications of our study for futureresearch and management practice.

Organizational Control Theory

In applying organization control theory toinnovation in hospitals, we follow Cardinal(2001) and define control as any process bywhich management directs attention andaligns the motivations and activities ofemployees towards organizational innovationobjectives (Ouchi, 1977, 1979; Eisenhardt, 1985;Govindarajan & Gupta, 1985; Jaeger & Baliga,1985; Kerr, 1985; Snell, 1992; Langfield-Smith& Smith, 2003). Distinct forms of controlinclude structural, market, cultural, input,output, process and integrative control(Cardinal, 2001; Lange, 2008).

In our study, we analyse the effects of formaland informal controls on innovation activityand innovation performance. We use the trans-mission channel dimension that distinguishesbetween formal and informal control mecha-nisms (Ouchi & Maguire, 1975; Ouchi, 1979;Lange, 2008). The transmission channeldimension is based on type of formality andthe level of bureaucracy, and distinguishesbetween social/cultural channels (informalcontrol) and administrative channels (formalcontrol) (Lange, 2008). We chose this dimen-sion because it is not limited to a certain indus-try, organizational size or set of businesscharacteristics.

Formal controls, and the associated controlmechanisms with high levels of bureaucracy,can be attributed to administrative channelsthat include formal processes, rules, regula-tions and structures. By contrast, more infor-mal controls, such as beliefs, values and normsand associated control mechanisms with lowlevels of bureaucracy, are assigned to social andcultural control mechanisms and are transmit-ted through social channels and corporateculture. Social and cultural control mecha-nisms are consistent with the concept of clancontrol, which is defined by Turner andMakhija (2006) as ‘the informal socializationmechanisms that take place in an organizationand that facilitate shared values, beliefs,and understandings among organizationalmembers.’ Goal incongruence between anorganization and its members is eliminatedthrough informal social interaction (Ouchi,1979; Eisenhardt, 1985; Turner & Makhija, 2006;Lange, 2008). Based on organizational controltheory, we chose two variables, each represent-ing one control mechanism of the transmissionchannel dimension.

Development of Hypotheses

Our dependent variables are the level of inno-vation performance and innovation activity.The meaning and measure of innovation per-formance are complex; this is particularly thecase in the service industries, where manyinnovations are process innovations. Maidiqueand Zirger (1985) describe innovation perfor-mance as the sum of all achievements of inno-vation projects and the implications of theseachievements for organizations and theirstakeholders. These various effects are relatedto market, organizational, quality and environ-mental issues (Balachandra & Friar, 1997). Thelevel of innovation activity is primarily deter-mined by the quantity of innovation projects.In addition, innovation activity is also influ-enced by the spectrum of an innovation port-folio, which depends on industry and marketconditions. In the case of hospitals, innovationsrange from product innovations (innovativetreatment or service offerings) to externalprocess innovations (inter-organizational pro-cesses) and internal process innovations(visible-to-patient processes and invisible-to-patient processes) (Schultz, Zippel-Schultz &Salomo, 2012).

Proactiveness and InnovationProcess Formalization

According to O’Reilly (1989), a social controlsystem is characterized by the existence ofcommon agreements among people withrespect to what constitutes appropriate atti-tudes and behaviour. Lange (2008) describessocial and cultural control as ‘carried by thebeliefs, values, and norms in the organization’.Following O’Reilly and Lange, we useproactiveness as the variable that representsthe social and cultural control mechanism(informal control) in terms of innovativebehaviour. Based on the proactiveness dimen-sion, as described by Venkatraman (1989),proactiveness comprises proactive behaviourin a continuous search for innovation andmarket opportunities as well as experimenta-tion with potential responses to changingenvironmental trends. Proactiveness reflectsan organization-wide drive of employees to beinnovative as a result of the existence of aninnovation culture. Moreover, proactivenessreflects a corporate culture with a strong atti-tude towards innovation. The desire to behighly innovative and to be perceived as aninnovative market participant is associatedwith an increased willingness to take risks.Proactiveness, as a characteristic of an inno-vation culture, concerns the attitude ofemployees towards innovation activities. Such



behaviour results in an increase in the quantityof innovation projects. According to O’Reilly(1989), innovation-related norms that arewidely shared and strongly held by membersof an organization strongly influence the gen-eration of new ideas and support in the imple-mentation of new approaches. Cultural norms,in which it is not only permissible but alsodesirable to challenge the status quo, are asso-ciated with an increasing likelihood of innova-tion (O’Reilly, 1989). Consequently, we presentthe following hypothesis:

Hypothesis 1: Proactiveness is positively relatedto innovation activity.

In addition to innovation activity, proacti-veness may affect innovation performance. Inan organization in which innovation isassigned a high priority, the effects of innova-tions will be intensively communicated. Insuch organizations, employees are expected toexert greater effort in achieving high-qualityresults. Organizations with a culture of highproactivity understand new and future envi-ronments and effectively promote best prac-tices and enhanced performance (Knight, 2000;Ussahawanitchakit, 2011). This logic leads tothe following hypothesis:

Hypothesis 2: Proactiveness is positively relatedto innovation performance.

Following Lange (2008), who describes admin-istrative controls as ‘formal structures and rou-tines, particularly through hierarchies ofauthority internal and external to the organiza-tion’, we consider innovation process formali-zation to be the variable that represents aformal control mechanism. A large amount ofresearch has investigated the positive effects offormalized innovation processes. Especially inthe field of new product development, processformalization is an established managementapproach (Henard & Szymanski, 2001; Kahn,Barczak & Moss, 2006; Kleinschmidt, deBrentani & Salomo, 2007; Schultz et al., 2013).Research has also demonstrated the relevanceof professional innovation processes withrespect to services (Drew, 1995; Song, Song &Di Benedetto, 2009). Innovation process for-malization plays an important role in service-oriented innovation processes, especially inrule-driven organizations such as hospitals(Doolin, 2002). Clearly specified rules, pro-cedures, goals and responsibilities are funda-mental to the efficiency of the innovationprocess because they provide employees withstructure, reduce ambiguity and enable them tocope with complex situations (Tatikonda &Montoya-Weiss, 2001; Bolton, 2004). These pre-requisites enable employees to offer more ideas

and to initiate more innovation projects. There-fore, we present the following hypothesis:

Hypothesis 3: Innovation process formalizationis positively related to innovation activity.

Innovation process formalization also has apositive effect on commercial innovation pro-ject success (Moenaert et al., 1994; Atuahene-Gima, 1996; Salomo, Weise & Gemünden,2007). Cooper and Kleinschmidt (1995) observethat a high-quality innovation process withtough decision points is the primary driver ofinnovation performance. Therefore, wepropose the following hypothesis:

Hypothesis 4: Innovation process formalizationis positively related to innovation performance.

Extent of a Dedicated Innovation Function

According to Damanpour (1991), centraliza-tion reflects the locus of authority and decisionmaking and refers to the extent to which deci-sion making is concentrated in an organization(Jansen, van den Bosch & Volberda, 2006). Cen-tralization has been studied in the innovationliterature and has been found to exert bothnegative (e.g., Damanpour 1991; Jansen, vanden Bosch & Volberda, 2006) and positiveeffects on innovation (e.g., Cardinal, 2001).Research that focuses on the central coordina-tion of innovation activities presents evidenceof positive effects of centralization. Hendersonand Cockburn (1994) demonstrate ways inwhich sophisticated organizing mechanismsthat coordinate R&D knowledge and activitiesresult in significant improvements in R&Dproductivity. The authors describe suchmechanisms as the architectural competenceof a firm with respect to R&D. Moreover, Clarkand Fujimoto (1990) emphasize the positiveeffects of central coordination of new productdevelopment teams. Dyer and Nobeoka (2000)argue that the creation of a separateorganizational unit responsible for accumulat-ing, storing, integrating and diffusing knowl-edge (e.g., production knowledge) is animportant component in explaining the learn-ing capability of a firm and its network part-ners. This unit represents a mechanism that isdesigned to centrally coordinate and sharevaluable knowledge throughout a firm’snetwork. The literature often focuses on therole of a dedicated alliance function, whichresembles the innovation function and isdescribed as an important component inbuilding the required expertise to gain a com-petitive advantage (Dyer, Kale & Singh, 2001;Fritsch & Lukas, 2001; Kale, Dyer & Singh,2002; Lichtenthaler & Lichtenthaler, 2004;Wassmer, Dussauge & Planellas, 2010).



Although the significance of both productand process innovation in the hospital marketis increasingly recognized, innovations areconsidered less vital to this market than inproduction-oriented industries (Salge & Vera,2009). The hospital sector often neglects to con-sider employees as a source of innovation.Process innovations and more substantialproduct innovations are often initiated byhigh-level hierarchical members – in smallerhospitals, perhaps by the CEO him- or herself.Moreover, the CEO is often responsible forvarious innovation management tasks thatinclude the coordination, steering and controlof innovation projects, with no delegation ofthese tasks to other individuals or teams. It isquestionable, from a resource perspective,whether it is appropriate for a CEO to beresponsible for such a broad range of innova-tion management tasks.

We describe a dedicated innovation functionas a person or group of people who are cen-trally involved in the initiation, selection, coor-dination, control and evaluation of innovationprojects (Dyer, Kale & Singh, 2001; Kale, Dyer& Singh, 2002). This function should include atleast one person other than the CEO. A dedi-cated function supports decision-making pro-cesses pertaining to innovation issues and thecoordination of innovation projects. It enablesa CEO to focus on the relevant decision pointsof the innovation process rather than onprocess organization and guidance.

A dedicated innovation function mayincrease internal and external visibility; it indi-cates to employees the importance of innova-tion and focuses their attention on innovationactivities (Dyer, Kale & Singh, 2001). Such afunction can provide support and resources toemployees to enable and encourage them to bemore innovative (Amabile et al., 1996). A dedi-cated innovation function can assist in situa-tions where employees perceive innovation asstrictly an upper management task, and it cantransform the organization into one whereinnovation is perceived as a process to whichthe entire organization contributes. Thus, wepresent the following hypothesis:

Hypothesis 5: The extent of a dedicatedinnovation function is positively related toproactiveness.

A dedicated innovation function has theresources to define specified rules, procedures,goals and responsibilities of the innovationprocess, thus fostering a more successfulimplementation of innovation processes. More-over, a dedicated innovation function operatesas an accumulation point for innovation-relatedknowledge. According to Kale, Dyer and Singh(2002), a dedicated function can act as a focal

point for learning and for leveraging bothexplicit and tacit lessons from prior andongoing initiatives. As noted by Dyer, Kale andSingh (2001), a dedicated function can improvethe management of specific aspects of the inno-vation life cycle through the codification ofexplicit innovation-management knowledge inthe form of guidelines and manuals. Therefore,we hypothesize as follows:

Hypothesis 6: The extent of a dedicated innova-tion function is positively related to innovationprocess formalization.

A dedicated function acts as a contact point foremployees and increases opportunities toinvolve them in innovation activities (Dyer,Kale & Singh, 2001). Focusing on the resourcesthat are necessary to initiate and realize inno-vation activities, Kale, Dyer and Singh (2002)describe a further effect of a dedicated alliancefunction, which can be transferred to the topicof innovation. Frequently, alliances fail or arenot realized because of the inability of apartner to mobilize resources. Moreover, in thecase of innovation, partners must worktogether to ensure adequate resources. In thiscontext, a dedicated innovation function can besupportive in two ways. First, this function canexploit its organizational legitimacy to reachacross divisions and functions to request theresources that are necessary to support inno-vation initiatives, thereby fulfilling an internalcoordination function. Second, the individualswho compose a dedicated innovation functioncan develop networks and determine the loca-tions of various useful resources within anorganization. Thus, these members can facili-tate reciprocal exchanges in support of innova-tion initiatives (Kale, Dyer & Singh, 2002). Thesecond point also implies that dedicated inno-vation functions support the participation ofbroader functional groups in the ideationprocess and may increase the acceptance ofinnovative initiatives across functional groupsand hierarchical levels. Consequently, wepropose the following hypothesis:

Hypothesis 7: The extent of a dedicated innova-tion function is positively related to innovationactivity.

The coordination of innovation projects by adedicated innovation function supportsknowledge accumulation and learning frompast innovation projects. The increasing extentof innovation-related responsibilities andtasks, the empowerment of a dedicated func-tion and the availability of resources enablethis function to concentrate on a broad spec-trum of innovation management-related tasks,including strategy-related tasks (e.g., the defi-nition of innovation strategies and alignment



with overall hospital strategy). Innovationprojects that are consistent with a hospital’sstrategic objectives receive more managementattention; therefore, the likelihood of attaininga project’s objectives increases. An importantbenefit of the existence of a dedicated innova-tion function is the establishment of systematicproject evaluations. Such evaluations enablemanagement to monitor innovation activitiesand to intervene early to ensure that innova-tion activities progress as intended (Kale, Dyer& Singh, 2002). Moreover, research on alli-ances has found that such functions play acrucial role in enhancing alliance capabilitiesof organizations and improving alliance per-formance (Rothaermel & Boeker, 2008; Kale &Singh, 2009), results that we believe are trans-ferable to the innovation context. This reason-ing leads to our final hypothesis:

Hypothesis 8: The extent of a dedicated innova-tion function is positively related to innovationperformance.

With respect to the hypotheses that the extentof a dedicated innovation function is positivelyrelated to proactiveness (H5), innovationprocess formalization (H6) and innovationactivity (H7), we also recognize the likelihoodof self-reinforcing effects, i.e., reverse causal-ity. An increase in innovation and innovationmanagement activities may lead to a greaterneed for a dedicated innovation function.However, we consider the causal directionstarting from the dedicated innovation func-tion to be dominant and thus only incorporatethe above hypotheses in our model. Theoverall theoretical framework, incorporatingall our hypotheses, is illustrated in Figure 1.

Research Method

Data Collection and Sample

We conducted a survey of hospitals inGermany. We contacted hospital management(hospital CEOs) directly via e-mail, providinga digital questionnaire. Additionally, all hospi-tals were informed in advance of the study andthe associated survey by e-mail and a news-letter article. To ensure comparability of hos-pitals, especially with respect to innovationactivities, we excluded university hospitals,small hospitals with fewer than 50 beds, day ornight clinics, hospitals with only outpatienttreatment and psychiatric hospitals. Of 1,382hospitals that received surveys, 183 returnedthem providing a response rate of 13 per cent.After plausibility checks, 158 questionnaireswere complete and useable in the analysis.Surveyed hospitals were located in 15 of the 16German federal states, exhibited all forms ofownership (36 per cent public, 48 per cent non-profit and 16 per cent private), and were ofvarious sizes (mean = 380 beds). The distribu-tion of hospital ownership and of hospital sizein terms of beds is approximately representa-tive of the German hospital market.

Methodological Approach to Data Analysis

A structural equation model approach wasused to empirically test our hypotheses. Thepartial least squares (PLS) path modellingmethod, developed by Wold, was used tomodel the hypotheses (Wold, 1982; Fornell &Cha, 1994; Cassel, Hackl & Westlund, 1999).We chose PLS because it can be used with arelatively small sample size and can handle

Figure 1. Research Framework



both reflective and formative measures (Chin,1998b; Hair et al., 2012). To estimate the struc-tural equation model, we used SmartPLS 2.0software (Ringle, Wende & Will, 2005). Toensure the significance of the path coefficients,the inference statistics are based on a boot-strapping procedure with 1,000 bootstrapsamples all equal in size to the original sample(Efron & Tibshirani, 1993). For the evaluationof the inner and outer models, we followed theguidelines of Hair et al. (2012).

Measures

To measure the selected constructs, we appliedexisting and already validated scales whenpossible. To ensure comprehension of theitems by hospital management, we discussedthe topics covered in expert workshops andmodified the wording of items to meet thespecific characteristics of the German hospitalmarket. For the reflective measurementmodels, the composite reliability (CR) and thefactor loadings per item are presented in thetext.

Innovation Performance (CR = 0.88)

To measure innovation performance, we con-sidered various objectives of innovation per-formance, following the approach of Maidiqueand Zirger (1985) and Balachandra and Friar(1997). Because innovations differ in theirobjectives, we included the following goals asitems in the measurement of innovation per-formance: (1) increase in employee satisfaction[0.65], (2) increase in patient satisfaction [0.75],(3) increase in treatment quality [0.74], (4)increase in revenues [0.75], (5) increase in effi-ciency [0.72] and (6) enhancement of hospitalimage [0.80]. Using a five-point Likert scaleranging from 1 (‘strongly disagree’) to 5(‘strongly agree’), we measured the extent towhich innovations contributed to these goals.

Innovation Activity

We define innovation activity as consisting ofthe current volume and range of innovationprojects in an organization’s innovation port-folio. Schultz, Zippel-Schultz and Salomo(2012) generate a list of 36 possible innovationactivities within three thematic clusters: intro-duction of new medical services, optimizationof hospital internal processes and optimizationof external processes. These innovation activ-ities were identified through a systematicanalysis of German hospital journals over aperiod of three years prior to this study andwere validated through expert interviews. Wefollowed Schultz, Zippel-Schultz and Salomo

(2012) in adapting and extending theirapproach, and we identified a list of nine inno-vation categories that represent the mostimportant German hospital innovation areas inrecent years, while covering a broad range ofdifferent innovation topics across the three the-matic clusters.

Specifically, the formative construct of inno-vation activity was measured by the followingnine items: ‘development of clinical pathways’,‘case management’, ‘development of newbusiness segments’, ‘enhancement of cross-sectoral patient care’, ‘division of labour ofhealth professionals in the hospital’, ‘establish-ment or development of hospital informationsystems’, ‘use of electronic health records’,‘physician-to-patient telemedicine’ and‘physician-to-physician telemedicine’. A highvalue for this variable indicates a large numberof innovation activities in the field in whichmost recent hospital innovations have beenmade. To obtain more specific informationabout the current status of innovations beingundertaken, we used a detailed scale ratherthan simply enquiring whether projects in thenine defined innovation categories were beingconducted. We thus employed a four-pointscale that included the following responses:‘no relevance’, ‘in planning’, ‘in development’and ‘realized’.

Proactiveness (CR = 0.88)

This variable measures proactive behaviourwith respect to innovation. Our scale is basedon the construct of proactiveness byVenkatraman (1989). We define proactivenessas activities of hospitals in seeking new oppor-tunities with a forward-looking perspective,e.g., seeking to introduce new services andprocesses before they are introduced by com-petitors (Ussahawanitchakit, 2011).

We adjusted the items and adapted thewording to suit specific hospital requirements.Our construct contains the following fouritems: (1) we are more likely to initiate activ-ities to which competitors must react ratherthan to react to the activities of competitors[0.78], (2) we are typically the first to introducenew health care offerings [0.85], (3) we are con-stantly seeking new opportunities to expandour target markets [0.79] and (4) we are alwaysthe first to be active in emerging market trends[0.82]. The construct was measured on a five-point Likert scale ranging from 1 (‘stronglydisagree’) to 5 (‘strongly agree’).

Innovation Process Formalization (CR = 0.90)

To measure innovation process formaliza-tion, we relied on well-established scales



of NPD process formality (Cooper &Kleinschmidt, 1995; Griffin, 1997; Cooper,1999; Kleinschmidt, de Brentani & Salomo,2007) and adjusted the four items to hospital-specific needs: (1) our innovation projects haveclearly defined phases [0.79], (2) our innova-tion process defines and contains specificactivities (e.g., feasibility studies) for eachproject phase [0.86], (3) the innovation processcontains well-defined decision points at whichdecisions regarding the future course (orapplicable termination) of the project are made[0.88] and (4) decisions regarding future inno-vation processes are clearly assigned to one ormore individuals [0.78]. The construct wasmeasured using a five-point Likert scaleranging from 1 (‘strongly disagree’) to 5(‘strongly agree’).

The extent of a Dedicated Innovation Function

We determine the extent of a dedicated innova-tion function by combining the following threequestions: (1) ‘Are the relevant innovation man-agement tasks conducted at all?’, (2) ‘Are therelevant innovation management tasks con-ducted by the CEO only, or does a dedicatedinnovation function exist?’ and (3) ‘To whatextent is the dedicated innovation functioninvolved in the following tasks?’. When theresponse to question (1) was ‘No’, the constructvalue was 0. When the response to question (1)was ‘Yes’, question (2) became relevant. Whenthe response to question (2) was ‘CEO only’, theconstruct value was 1. When the response toquestion (2) was ‘A dedicated innovation func-tion does exist’, question (3) became relevant.The third question specified five differentinnovation-related tasks and asked respond-ents to indicate the degree of involvement ofthe dedicated innovation function in each. Thefive tasks, each of which reflects an aspect ofinnovation management, were: (1) the defini-tion of hospital strategy, (2) the initiation ofinnovation projects, (3) the selection of innova-tion projects, (4) the control of the innovationportfolio and (5) the evaluation of innovationactivities. Question (3) was measured using afive-point Likert scale. For the combined con-struct, ‘extent of a dedicated innovation func-tion’, the lowest value was determined as 2 andthe highest value as 6. The mean of this scalewas used for the combined construct. Overall,the combined construct has a range of 0 to 6. Avalue of 0 indicates a hospital in which innova-tion tasks are not conducted at all. A value of 1indicates that relevant innovation tasks are con-ducted only by the CEO of a hospital. Values of2 through 6 reflect various levels of relevantinnovation tasks conducted by a dedicatedinnovation function.

Control Variables

In testing our hypotheses, we controlled forthe following three variables: hospital owner-ship, hospital size and innovation activity byCEOs. Hospital ownership (public, non-profitor private) may explain different innovationactivities. Public and non-profit hospitals maynot have the same access to innovation budgetsas private hospitals; hence, this difference maypositively influence innovation activity. Inaddition, private hospitals may be managedmore extensively, which might also influenceinnovation initiatives (Aarons, Sommerfeld &Walrath-Greene, 2009). The model includesinformation on public and non-profit hospitalsand uses private hospitals as a reference group.

To control for hospital size, we used numberof beds as a proxy. Larger hospitals may have abroader spectrum of innovation activities toinvest in and therefore superior innovationperformance (Tiemann & Schreyögg, 2009).Larger hospitals have more resources ingeneral to distribute to different tasks, includ-ing innovation activities. Data for the controlvariables of hospital ownership and hospitalsize were collected from the official qualityreports of the hospitals.

Finally, the CEOs were asked how often ini-tiatives for innovation activities originate withhospital management, which we use as acontrol variable to avoid respondense bias bymanagement with respect to their own inno-vation activities. This scale was measured witha five-point Likert scale ranging from 1(‘strongly disagree’) to 5 (‘strongly agree’).

Results

To ensure the robustness of our partial leastsquares structural equation modelling (PLS-SEM) results, we employed the common ruleof thumb that the sample size should be atleast ten times the maximum number of pathsconnected to any construct in both the outermodel (i.e., the number of formative indicatorsper construct) and the inner model (i.e., thenumber of path relationships directed at a par-ticular construct) (Chin, Marcolin & Newsted,2003; Hair et al., 2012).

In reflective measurement models, thestandardized indicator loading should at leastexceed a threshold of 0.70 to define the indica-tor as reliable (Chin, 1998b; Hulland, 1999;Henseler, Ringle & Sinkovics, 2009). Only oneindicator (increase in employee satisfaction) of‘innovation performance’ has a smaller value(0.65). We nevertheless decided to include thisindicator as part of the construct to guaranteecontextual validity. All of the relationshipsbetween the indicators and their constructs are



highly significant (p < 0.01). Table 1 presentsall of the items of the reflective measurementsas well as the estimated loadings, means andstandard deviations.

Additionally, for all reflective measurementmodels, the average variance extracted (AVE)is well above the threshold of 0.50 (Bagozzi &Yi, 1988). To overcome Cronbach’s alpha limi-tation, which arises from the assumption thatall indicators are equally reliable (tau-equivalence), we used composite reliabilitywhich does not assume tau-equivalence and istherefore better suited for PLS-SEM (Bagozzi& Yi, 1988; Hair et al., 2012). All values forCronbach’s alpha and composite reliability arewell above the threshold of 0.7 (Hair et al.,1998).

We tested for potential non-response bias bycomparing the included 158 cases with the

cases that we eliminated owing to missingvalues for one or more items. We found nosignificant differences with respect to any ofthe endogenous variables. We used theFornell–Larcker criterion to estimate the dis-criminant validity (Fornell & Larcker, 1981;Fornell & Cha, 1994). The AVE for each con-struct should exceed the construct’s highestsquared correlation with any other construct.In our model, all square roots of AVE exceedtheir correlations with any other construct. TheFornell–Larcker criterion is thus met and thediscriminant validity is confirmed. The corre-lations and AVE values are presented inTable 2. In addition, we checked cross-loadings. All indicators load highest on theircorresponding constructs.

For the formative measurement of innova-tion activity, it is important to cover a broad

Table 1. Overview of Reflective Measurement Items (translated from German) (Scale 1–5, except ‘Extentof a dedicated innovation function’)

Latent variable Item Loadings Mean (SD)

Innovationperformance

Increase in employee satisfaction 0.65 3.27 (0.70)Increase in patient satisfaction 0.75 3.69 (0.76)Increase in treatment quality 0.74 3.96 (0.70)Increase in revenues 0.75 3.73 (1.00)Increase in efficiency 0.72 3.61 (0.83)Enhancement of hospital image 0.80 3.85 (0.89)

Proactiveness We are more likely to initiate activities towhich competitors must react rather thanto react to the activities of competitors

0.78 3.75 (0.86)

We are typically the first to introduce newhealth care offerings

0.85 3.16 (0.89)

We are constantly seeking new opportunitiesto expand our target markets

0.79 3.78 (0.83)

We are always the first to be active inemerging market trends

0.82 2.80 (0.82)

Innovation processformalization

Our innovation projects have clearly definedphases

0.79 2.89 (1.04)

Our innovation process defines and containsspecific activities (e.g., feasibility studies)for each project phase

0.86 2.82 (1.01)

The innovation process contains well-defineddecision points at which decisionsregarding the future course (or applicabletermination) of the project are made

0.88 3.00 (0.95)

Decisions regarding the future innovationprocess are clearly assigned to one or morepersons

0.78 3.37 (1.00)

Extent of a dedicatedinnovation function

Extent of a dedicated innovationfunction (Single item construct)

1.00 1.00

All loadings are significant at the 0.01 level.



range of hospital innovation topics. To ensurethat indicators do not designate the sameaspects and to evaluate the reliability of theformative constructs, we test for multicollin-earity (Diamantopoulos & Siguaw, 2006;Petter, Straub & Rai, 2007). The variance infla-tion factor (VIF) is considered a good indicatorof multicollinearity. Diamantopoulos andSiguaw (2006) recommend a VIF value of lessthan 3.3. The highest value of VIF for all nineindicators is 1.4, well below this threshold.With the exception of two indicators, theweights are above the threshold of 0.1 (and

above 0.2 for four indicators). All indicatorswere retained as part of the construct to ensurecontextual validity. In summary, the reflectiveand formative measurement models can beinterpreted as valid and reliable. Table 3 pre-sents all items of the formative measurementas well as the estimated weights, means andstandard deviations.

We included the control variables as a forma-tive construct in our PLS-SEM. This constructshows an effect on informal control mecha-nisms as well as on innovation activity. Table 2provides the correlation matrix for all the

Table 2. Fornell-Larcker Criterion and Correlations (AVE on the diagonal [bold]; correlations in the lowerleft triangle)

1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

1. Innovationperformance

.54

2. Public hospitals −.10 –3. Non-profit hospitals .00 −.71** –4. Private hospitals .13 −.33** −.42** –5. Number of beds

(# 100 beds).05 .39** −.21** −.22** –

6. Innovation activityof CEO

.26** .14 −.12 −.02 .13 –

7. Innovation activity .31** .16* −.07 −.11 .26** .25** –8. Proactiveness .41** .02 .00 −.03 .32** .30** .43** .669. Innovation process

formalisation.34** −.01 .10 −.13 .03 .22** .29** .41** .69

10. Extent of a dedicatedinnovation function

.02 .06 −.03 −.05 .27** .26** .36** .28** .25** –

*p < .05; **p < .01

Table 3. Overview of Formative Measurement Items (translated from German)

Latent variable Item Weights Mean (SD)

Innovation activity Development of clinical pathways 0.15 1.44 (1.06)Case management 0.19 1.80 (1.03)Development of new business segments 0.03 1.73 (0.97)Enhancement of cross-sectoral patient care 0.32 1.77 (1.01)Division of labour of health professionals

in the hospital0.35 1.61 (1.10)

Establishment or development of hospitalinformation systems

0.24 2.06 (1.04)

Use of electronic health records −0.17 1.51 (0.94)Physician-to-patient telemedicine 0.50 0.84 (1.02)Physician-to-physician telemedicine 0.11 1.90 (1.22)



model constructs. There are significant correla-tion effects for certain individual control vari-ables, particularly the innovation activity of theCEO and hospital size. Table 2 shows, inter alia,that large hospitals are likely to be public hos-pitals and have a dedicated innovation func-tion. Therefore, larger and more complexorganizations appear to be more likely torequire dedicated innovation functions.

Hypotheses 1 and 2, which propose positiveeffects of the social and cultural channel (infor-mal control), as represented by theproactiveness construct, on innovation activity(H1) and innovation performance (H2), areboth supported, with high degrees of signifi-cance (p < 0.01). Similarly, hypotheses 3 and 4also propose a positive impact on innovationactivity (H3) and innovation performance (H4)through the administrative channel (formalcontrol), represented by the innovation processformalization construct. The path coefficientfor H4 is positive and highly significant; thus,H4 is supported. However, the relationshipbetween innovation process formalization andinnovation activity is not significant, and H3 isrejected. Hypotheses 5–8 concern the influenceof the existence of a dedicated innovation func-tion on the different control mechanisms(informal: H5 and formal: H6), innovationactivity (H7) and innovation performance(H8). The impact of the existence and extent ofa dedicated innovation function on the differ-ent control mechanisms are, as expected, posi-tive. Both path coefficients are positive andsignificant (informal control: p < 0.05 andformal control: p < 0.01). Moreover, the effect

on innovation activity is positive and signifi-cant (p < 0.05). Hypotheses 5, 6 and 7 are thussupported. The direct effect of the extent of adedicated innovation function on innovationperformance is significant but surprisinglynegative. Hypothesis 8 is thus rejected.

In summary, the informal control mecha-nism has a positive effect on innovation activ-ity and innovation performance. The formalcontrol mechanism has a significant positiveeffect on innovation performance but not oninnovation activity. The extent of a dedicatedinnovation function has a positive effect onboth control mechanisms and innovation activ-ity. Contrary to expectations, the innovationfunction’s direct effect on innovation perfor-mance is negative. Additionally, we includethe direct effect of innovation activity on inno-vation performance in our model to consider apartially mediated effect of the control mecha-nisms on innovation performance. The pathcoefficient is positive and significant (p < 0.05).Table 4 presents the results of the structuralmodel, including the path coefficients and sig-nificance levels, obtained using the bootstrap-ping method.

The model explains 28 per cent of the vari-ance of the latent endogenous variables, with avalue for R2 of 0.28, well above the threshold of0.19 (Chin, 1998a). We calculated f 2 for all sig-nificant paths, to consider changes in R2 valuesarising from the relative effects of particularexogenous latent variables on endogenouslatent variables. For all relevant R2 values, theeffect sizes f2 are well above the threshold of0.02 (Cohen, 1988). We ran blindfolding pro-

Table 4. PLS-SEM Results

Hypotheses Path coefficients

H1 Proactiveness > Innovation activity 0.30**H2 Proactiveness > Innovation performance 0.25**H3 Innovation process formalization > Innovation activity n.s.H4 Innovation process formalization > Innovation performance 0.19**H5 Extent of a dedicated innovation function > Proactiveness 0.17*H6 Extent of a dedicated innovation function > Innovation process

formalization0.21**

H7 Extent of a dedicated innovation function > Innovation activity 0.16*H8 Extent of a dedicated innovation function > Innovation performance −0.22**

Controls > Proactiveness 0.35**Controls > Innovation process formalization n.s.Controls > Innovation activity 0.18*Controls > Innovation performance n.s.

R2 = 0.28

**p < 0.01; *p < 0.05; n.s. = non-significant (p > 0.05); controls included as one formative construct.



cedures, obtaining Stone–Geisser criterion Q2

values above the threshold level of zero. Addi-tionally, we used the Stone–Geisser criterion toevaluate relative predictive relevance (q2) of therelevant paths. All values for q2 are well abovethe threshold of 0.02 (Chin, 1998b; Henseler,Ringle & Sinkovics, 2009). Therefore, theimpact of the relevant exogenous variableson their latent endogenous successors isconfirmed.

To ensure the robustness of our model, weconducted additional analyses. First, we esti-mated the model with control variables incor-porated into it as single items rather thanas a formative construct. The results wereunchanged. We therefore decided to retain thecontrols as a formative construct to avoidplacing any unnecessary stress on the model.Second, we applied innovation performance asa formative construct in our model. Overall,the results remain unchanged. The explainedvariance of innovation performance is slightlyhigher under the formative model. However,due to potential challenges with formativelatent variables of endogenous constructs(Cadogan & Lee, 2013), we believe that ahigher validity of our results is achieved byapplying a reflective measurement.

In addition to the PLS-SEM, we analysed allpaths for possible curvilinear relationships. Wedetected a significant curvilinear relationship(U-shaped) for the path between innovationprocess formalization and innovation perfor-mance. Furthermore, we checked for the pres-ence of moderating effects for all relevantrelationships in our model but found none thatare significant. With respect to the measure-ment of the extent of a dedicated innovationfunction, we checked for two additional alter-natives. First, we examined responses to thequestion, ‘To what extent is the dedicatedinnovation function involved in these tasks?’(that is, each of the five indicated tasks) in iso-lation from the other questions. Second, weadded the question, ‘Are the relevant innova-tion management tasks conducted by the CEOonly, or does a dedicated innovation functionexist?’ – assigning a value of 0 to the constructin cases where a dedicated innovation functiondid not exist. The results for both versionswere equivalent to those obtained with thefinal version of the questionnaire, in which weposed the additional question, ‘Are the rel-evant innovation management tasks conductedat all?’ to create the construct.

Discussion and ManagementImplications

Based on control theory, we have developed atheoretical framework that links proactiveness

(informal control) and innovation process for-malization (formal control) to innovation activ-ity and innovation performance (see Figure 1).Our study contributes to and extends the exist-ing literature by examining the effects of bothformal and informal controls on innovationactivity and innovation performance. In par-ticular, our study answers the question: ‘Howdo different control mechanisms influenceinnovation activity and innovation perfor-mance in hospitals?’ (Cardinal, 2001; Cardinal,Sitkin & Long, 2004; Kirsch, Ko & Haney,2010). In addition, we contribute to the litera-ture by analysing the role of dedicated innova-tion functions in innovation process controland innovation performance (Cardinal, 2001;Kale, Dyer & Singh, 2002). We also include theextent of a dedicated innovation function inthe model and analyse its effects on both infor-mal and formal control mechanisms and itsdirect effects on innovation activity and inno-vation performance.

The demonstrated positive effect of infor-mal control mechanisms on innovation activityis consistent with the findings of Khatri,Brown and Hicks (2009) and O’Reilly (1989).Hospitals often fail to emphasize innovation intheir organizational culture, and innovation isfrequently not viewed by upper level manage-ment as a primary task; innovation is consid-ered even less of a priority by employees.Compared to other industry sectors, hospitalshave limited experience and knowledge ininnovation activities and procedures. There-fore, increasing proactivity among hospitalmanagement and employees with respect toinnovation would appear to be a prerequisiteto increasing the innovation capabilities of hos-pitals. This point is highlighted by the addi-tional positive effect of informal control oninnovation performance.

With respect to formal control mechanisms,our model shows no significant effect on inno-vation activity. The often-cited negative effectsof process formalization on innovation –caused, for example, by too much rigidity –might weaken the expected positive effects(Sethi & Iqbal, 2008). The positive effect oninnovation performance reflects a significantinfluence of formalized innovation processes.This may be explained by the high proportionand importance of incremental innovations inhospitals. Incremental innovations benefitfrom the existence of structure, definitions andrules (Calantone, Harmancioglu & Droge,2010). Moreover, the curvilinear (U-shaped)relationship between innovation process for-malization and innovation performance sug-gests that formalization becomes effective onlyif it is fully and comprehensively implementedin hospitals. A half-hearted establishment of



formal control mechanisms may contrast withthe relatively process-orientated culture ofhospitals, generating conflict (Schultz et al.,2013).

In addition to informal control mechanisms,hospital management could benefit fromformal control mechanisms. A high level ofprocess formalization, especially in the case ofincremental innovation, can improve thequality of innovation by providing structure,procedures and rules for innovation. Hospitalmanagement might encourage proactivebehaviour among employees with respect toinnovation and simultaneously provide anextensive set of structures, definitions andrules that supports the innovation initiatives ofemployees.

We also examine the contribution of theexistence and empowerment of a dedicatedinnovation function in hospitals. Our studyidentifies a significant number of hospitalCEOs who execute innovation managementindividually, including rather operations-related innovation tasks. The provision ofdedicated resources to innovation manage-ment and the signal that innovation is notexclusively a management activity, in conse-quence of establishment of a dedicated inno-vation function, might explain the significantpositive effect of the extent of a dedicatedinnovation function on informal controlmechanisms. There is a further positive rela-tionship between the extent of a dedicatedinnovation function and the formal controlmechanism (innovation process formaliza-tion), both of which have a high need forresources (especially human resources) toensure that all formalization-related pro-cedures and tools are utilized. Hospitalmanagers must recognize that process for-malization requires resources and should notbe conducted solely by a CEO. The creationand empowerment of a dedicated innovationfunction can ensure the consistent availabilityof resources for and attention to innovation,so that all innovation-related tasks meettheir objectives. Our results indicate that adedicated innovation function that is pro-vided with adequate resources may increasethe effectiveness of formal and informalcontrols. Our results also indicate that inno-vation activity is more prevalent in hospitalswith an empowered, dedicated innovationfunction.

An unexpected result of our study is thenegative direct relationship between the extentof a dedicated innovation function and innova-tion performance. The measured negativeeffect of a dedicated innovation function mayarise from increasingly intensified centraliza-tion of innovation tasks, some of which had

been successfully performed in a decentral-ized manner. Research has revealed possiblenegative effects of centralization (Damanpour,1991), effects that are evident in our study.Because centralization can limit communica-tion channels, employee involvement in inno-vation may decline, and accordingly, thedegree to which innovations reflect employeeneeds may diminish, leading to a decline inthe quality of ideas (Nord & Tucker, 1987;Sheremata, 2000; Jansen, van den Bosch &Volberda, 2006). Additionally, a low level ofacceptance of a dedicated innovation functionby employees could hinder implementation,as could low management support. Owing tothe delegation of innovation-related tasks to adedicated innovation function, the attentionand support of CEOs may decline. All thesepossible negative effects should be taken intoconsideration by hospital management whenestablishing a dedicated innovation function.The acceptance of a central dedicated innova-tion function by employees and continuedprovision of management support and atten-tion to innovation-related topics can andshould be actively managed by hospitalmanagement.

The model estimations demonstrate anexpected direct positive effect of innovationactivity on innovation performance, an effectthat we considered obvious and therefore didnot include among our hypotheses. A largenumber (quantity) of state-of-the-art innova-tion projects in a broad but relevant range(spectrum of the innovation portfolio) evi-dences a high priority for innovation withinorganizations. As the number of innovationprojects increases, experience and knowledgealso increase. A higher level of experienceenhances R&D productivity and leads toinnovation projects pursuing broader objec-tives, thus having a greater influence on hos-pital performance. Therefore, hospitalmanagement should devote greater attentionto innovation activity and ensure a broadspectrum of innovations to address andinvolve all stakeholders (patients, employeesand shareholders) and to increase or enhanceemployee satisfaction, patient satisfaction,treatment quality, revenues, efficiency andhospital image.

In summary, we argue that hospital man-agement should devote greater attention toinformal and formal control mechanisms toincrease innovation activity and performance.Moreover, hospital management should con-sider the creation and empowerment of a dedi-cated innovation function to take advantage ofthe positive effects on innovation activity andon informal and formal control mechanismsassociated with such a function.



Limitations and Suggestions forFurther Research

Our study has certain limitations that mayguide future research. We were able to use arich dataset and control for several types ofbiases; however, the robustness of the resultsshould be interpreted with caution. Becauseour data were mostly obtained from a singletype of informant – hospital CEOs – we mayencounter problems related to common sourcebias. In particular, responses relating to man-agement tasks could be overly positive, andresponses concerned with the work of otherfunctions, such as dedicated innovation func-tions, could be overly negative. The validity offuture studies also could be strengthenedthrough the use of different instruments tomeasure the relevant constructs instead ofrelying on a single instrument. The cross-sectional study design confines the results to aspecific point in time. Therefore, reverse cau-sality was not analysed in this study. Becausecertain reverse causalities may exist, it wouldbe of interest to conduct further research,employing a longitudinal study design. Byconsidering different types of innovationgoals, we obtained a comprehensive measureof innovation performance. To strengthen thevalidity of this measure, future research mightconsider additional internal and externalstakeholders, such as employees, patients andregulators.

In applying control theory to our investiga-tion, we examined only the transmissionchannel dimension. Future research mightbenefit from considering other dimensions.Moreover, other formal and informal controlmechanisms, in addition to proactiveness andinnovation process formalization, could beconsidered in future studies. Complementaryassets may influence the effectiveness of thededicated functions as well as the analysedcontrol mechanisms. Future research may con-sider these possible boundary conditions, suchas top management support, the level of inno-vation experience, the maturity of the dedi-cated innovation function and acceptance byhospital staff.

Although we distinguished between innova-tion activity and innovation performance in thisstudy, we did not differentiate by innovationtype (process vs. product or incremental vs.radical). Because innovation activities in hospi-tals are generally characterized by heterogene-ity, it would be interesting to investigate thepossible moderating influences of innovationtypes. Finally, this study was conducted only inthe German hospital market. In terms ofgeneralizability, it would be interesting toinvestigate service industries worldwide.

References

Aarons, G., Sommerfeld, D. and Walrath-Greene, C.(2009) Evidence-Based Practice Implementation:The Impact of Public versus Private SectorOrganization Type on Organizational Support,Provider Attitudes, and Adoption of Evidence-Based Practice. Implementation Science, 4, 83.

Alexander, J.A., Weiner, B.J., Shortell, S.M., Baker,L.C. and Becker, M.P. (2006) The Role ofOrganizational Infrastructure in Implementationof Hospitals’ Quality Improvement. HospitalTopics, 84, 11–20.

Amabile, T.M., Conti, R., Coon, H., Lazenby, J. andHerron, M. (1996) Assessing the Work Environ-ment for Creativity. Academy of ManagementJournal, 39, 1154–84.

Atuahene-Gima, K. (1996) Differential Potency ofFactors Affecting Innovation Performance inManufacturing and Services Firms in Australia.Journal of Product Innovation Management, 13,35–52.

Bagozzi, R.P. and Yi, Y. (1988) On the Evaluation ofStructural Equation Models. Journal of theAcademy of Marketing Science, 16, 74–94.

Balachandra, R. and Friar, J.H. (1997) Factors forSuccess in R&D Projects and New Product Inno-vation: A Contextual Framework. IEEE Transac-tions on Engineering Management, 44, 276–87.

Bitner, M.J. and Brown, S.W. (2008) The ServiceImperative. Business Horizons, 51, 39–46.

Bolton, S.C. (2004) A Simple Matter of Control?NHS Hospital Nurses and New Management.Journal of Management Studies, 41, 317–33.

Cadogan, J.W. and Lee, N. (2013) Improper Use ofEndogenous Formative Variables. Journal of Busi-ness Research, 66, 233–41.

Calantone, R.J., Harmancioglu, N. and Droge, C.(2010) Inconclusive Innovation ‘Returns’: A Meta-Analysis of Research on Innovation in NewProduct Development. Journal of Product Innova-tion Management, 27, 1065–81.

Cardinal, L.B. (2001) Technological Innovation inthe Pharmaceutical Industry: The Use ofOrganizational Control in Managing Researchand Development. Organization Science, 12, 19–36.

Cardinal, L.B., Sitkin, S.B. and Long, C.P. (2004) Bal-ancing and Rebalancing in the Creation and Evo-lution of Organizational Control. OrganizationScience, 15, 411–31.

Carman, J.M., Shortell, S.M., Foster, R.W., Hughes,E.F., Boerstler, H., O’Brien, J.L. and O’Connor,E.J. (1996) Keys for Successful Implementation ofTotal Quality Management in Hospitals. HealthCare Management Review, 21, 48–60.

Cassel, C.M., Hackl, P. and Westlund, A.H. (1999)Robustness of Partial Least-Squares Method forEstimating Latent Variable Quality Structures.Journal of Applied Statistics, 26, 435–46.

Cheah, J. (2000) Clinical Pathways – An Evaluationof its Impact on the Quality of Care in an AcuteCare General Hospital in Singapore. SingaporeMedical Journal, 41, 335–46.

Chin, W.W. (1998a) Issues and Opinion on Struc-tural Equation Modeling. MIS Quarterly, 22, 7–16.

Chin, W.W. (1998b) The Partial Least SquaresApproach to Structural Equation Modeling. In



Marcoulides, G.A. (ed.), Modern Methods For Busi-ness Research. Lawrence Erlbaum, Hillsdale, NJ,295–336.

Chin, W.W., Marcolin, B.L. and Newsted, P.R. (2003)A Partial Least Squares Latent Variable ModelingApproach for Measuring Interaction Effects:Results from a Monte Carlo Simulation Study andan Electronic-Mail Emotion/Adoption Study.Information Systems Research, 14, 189–217.

Clark, K.B. and Fujimoto, T. (1990) The Power ofProduct Integrity. Harvard Business Review, 68,107–18.

Cohen, J. (1988) Statistical Power Analysis for theBehavioural Sciences. Lawrence Erlbaum Associ-ates, Hillsdale, NJ.

Cooper, R.G. (1984) The Performance Impact ofProduct Innovation Strategies. European Journal ofMarketing, 18, 5–54.

Cooper, R.G. (1990) Stage-Gate Systems – A NewTool For Managing New Products. Business Hori-zons, 33, 44–54.

Cooper, R.G. (1999) From Experience: The InvisibleSuccess Factors in Product Innovation. Journal ofProduct Innovation Management, 16, 115–33.

Cooper, R.G. (2008) Perspective: The Stage-Gate®Idea-to-Launch Process – Update, What’s New,and NexGen Systems. Journal of Product Innova-tion Management, 25, 213–32.

Cooper, R.G. and Kleinschmidt, E.J. (1995) Bench-marking the Firm’s Critical Success Factors inNew Product Development. Journal of ProductInnovation Management, 12, 374–91.

Damanpour, F. (1991) Organizational Innovation: AMeta-Analysis of Effects of Determinants andModerators. Academy of Management Journal, 34,555–90.

Diamantopoulos, A. and Siguaw, J.A. (2006) Forma-tive Versus Reflective Indicators inOrganizational Measure Development: A Com-parison and Empirical Illustration. British Journalof Management, 17, 263–82.

Djellal, F. and Gallouj, F. (2005) Mapping InnovationDynamics in Hospitals. Research Policy, 34, 817–35.

Doolin, B. (2002) Enterprise Discourse, ProfessionalIdentity and the Organizational Control of Hos-pital Clinicians. Organization Studies, 23, 369–90.

Douglas, T.J. and Judge, W.Q. (2001) Total QualityManagement Implementation and CompetitiveAdvantage: The Role of Structural Control andExploration. Academy of Management Journal, 44,158–69.

Drew, S.A.W. (1995) Strategic Benchmarking: Inno-vation Practices in Financial Institutions. Interna-tional Journal of Bank Marketing, 13, 4–16.

Dyer, J.H. and Nobeoka, K. (2000) Creating andManaging a High-Performance Knowledge-Sharing Network: The Toyota Case. Strategic Man-agement Journal, 21, 345–67.

Dyer, J.H., Kale, P. and Singh, H. (2001) How ToMake Strategic Alliances Work. MIT Sloan Man-agement Review, 42, 37–43.

Efron, B. and Tibshirani, R. (1993) An Introduction tothe Bootstrap. Chapman & Hall, New York.

Eisenhardt, K.M. (1985) Control: Organizationaland Economic Approaches. Management Science,31, 134–49.

Fitzsimmons, J.A. and Fitzsimmons, M.J. (2008)Service Management: Operations, Strategy, Informa-tion Technology. McGraw-Hill/Irwin, Boston,MA.

Fornell, C. and Cha, J. (1994) Partial Least Squares.In Bagozzi, R.P. (ed.), Advanced Methods of Market-ing Research, Blackwell Publishers, Cambridge,MA, pp. 52–78.

Fornell, C. and Larcker, D.F. (1981) EvaluatingStructural Equation Models with UnobservableVariables and Measurement Error. Journal of Mar-keting Research, 18, 39–50.

Fottler, M.D. (1987) Health Care OrganizationalPerformance: Present and Future Research.Journal of Management, 13, 367–91.

Fritsch, M. and Lukas, R. (2001) Who Cooperates onR&D? Research Policy, 30, 297–312.

Gallouj, F. and Savona, M. (2009) Innovation in Ser-vices: A Review of the Debate and a ResearchAgenda. Journal of Evolutionary Economics, 19,149–72.

Govindarajan, V. and Gupta, A.K. (1985). LinkingControl System to Business Unit Strategy: Impacton Performance. Accounting, Organizations &Society, 10, 51–66.

Griffin, A. (1997) PDMA Research on New ProductDevelopment Practices: Updating Trends andBenchmarking Best Practices. Journal of ProductInnovation Management, 14, 429–58.

Hair, J.F., Anderson, R.E., Tatham, R.L. and Black,W.C. (1998) Multivariate Data Analysis. Prentice-Hall, Upper Saddle River, NJ.

Hair, J., Sarstedt, M., Ringle, C. and Mena, J. (2012)An Assessment of the Use of Partial Least SquaresStructural Equation Modeling in MarketingResearch. Journal of the Academy of MarketingScience, 40, 414–33.

Henard, D.H. and Szymanski, D.M. (2001) WhySome New Products Are More Successful ThanOthers. Journal of Marketing Research, 38, 362–75.

Henderson, R. and Cockburn, I. (1994) MeasuringCompetence? Exploring Firm Effects in Pharma-ceutical Research. Strategic Management Journal,15, 63–84.

Henseler, J., Ringle, C.M. and Sinkovics, R.R. (2009)The Use of Partial Least Squares Path Modeling inInternational Marketing. Advances in InternationalMarketing, 8, 277–319.

Hulland, J. (1999) Use of Partial Least Squares (PLS)in Strategic Management Research: A Review ofFour Recent Studies. Strategic ManagementJournal, 20, 195–204.

Jaeger, A.M. and Baliga, B.R. (1985) ControlSystems and Strategic Adaptation: Lessons fromthe Japanese Experience. Strategic ManagementJournal, 6, 115–34.

Jansen, J.J.P., van den Bosch, F.A.J. and Volberda,H.W. (2006) Exploratory Innovation, ExploitativeInnovation, and Performance: Effects ofOrganizational Antecedents and EnvironmentalModerators. Management Science, 52, 1661–74.

Kahn, K.B., Barczak, G. and Moss, R. (2006) Perspec-tive: Establishing an NPD Best Practices Frame-work. Journal of Product Innovation Management,23, 106–16.

Kale, P. and Singh, H. (2009) Managing StrategicAlliances: What Do We Know Now, and Where



Do We Go From Here? Academy of ManagementPerspectives, 23, 45–62.

Kale, P., Dyer, J.H. and Singh, H. (2002) AllianceCapability, Stock Market Response, and LongTerm Alliance Success: The Role of the AllianceFunction. Strategic Management Journal, 23, 747–67.

Kerr, J.L. (1985) Diversification Strategies andManagerial Rewards: An Empirical Study.Academy of Management Journal, 28, 155–79.

Khatri, N., Brown, G.D. and Hicks, L.L. (2009) Froma Blame Culture to a Just Culture in Health Care.Health Care Management Review, 34, 312–22.

Kimberly, J.R. and Evanisko, M.J. (1981)Organizational Innovation – The Influence ofIndividual, Organizational, and ContextualFactors on Hospital Adoption of Technologicaland Administrative Innovations. Academy of Man-agement Journal, 24, 689–713.

Kirsch, L.J., Ko, D.-G. and Haney, M.H. (2010)Investigating the Antecedents of Team-BasedClan Control: Adding Social Capital as a Predic-tor. Organization Science, 21, 469–89.

Kleinschmidt, E.J., de Brentani, U. and Salomo, S.(2007) Performance of Global New ProductDevelopment Programs: A Resource-Based View.Journal of Product Innovation Management, 24, 419–41.

Knight, G. (2000) Entrepreneurship and MarketingStrategy: The SME Under Globalization. Journal ofInternational Marketing, 8, 12–32.

Lange, D. (2008) A Multidimensional Conceptua-lization of Organizational Corruption Control.Academy of Management Review, 33, 710–29.

Langfield-Smith, K. and Smith, D. (2003) Manage-ment Control Systems and Trust in OutsourcingRelationships. Management Accounting Research,14, 281–307.

Lansisalmi, H., Kivimaki, M., Aalto, P. andRuoranen, R. (2006) Innovation in Healthcare: ASystematic Review of Recent Research. NursingScience Quarterly, 19, 66–72.

Lichtenthaler, U. and Lichtenthaler, E. (2004) Alli-ance Functions: Implications of the InternationalMulti-R&D-Alliance Perspective. Technovation,24, 541–52.

Lukas, C.V., Holmes, S.K., Cohen, A.B., Restuccia, J.,Cramer, I.E., Shwartz, M. and Charns, M.P. (2007)Transformational Change in Health CareSystems: An Organizational Model. Health CareManagement Review, 32, 309–20.

Maidique, M.A. and Zirger, B.J. (1985) The NewProduct Learning Cycle. Research Policy, 14, 299–313.

McCracken, M.J., McIlwain, T.F. and Fottler, M.D.(2001) Measuring Organizational Performance inthe Hospital Industry: An Exploratory Compari-son of Objective and Subjective Methods. HealthServices Management Research, 14, 211–19.

Moenaert, R.K., Souder, W.E., de Meyer, A. andDeschoolmeester, D. (1994) R&D-Marketing Inte-gration Mechanisms, Communication Flows, andInnovation Success. Journal of Product InnovationManagement, 11, 31–45.

Nord, W.R. and Tucker, S. (1987) ImplementingRoutine and Radical Innovations. Lexington Books,Lexington, MA.

Ordanini, A. and Parasuraman, A. (2011) ServiceInnovation Viewed through a Service-DominantLogic Lens: A Conceptual Framework andEmpirical Analysis. Journal of Service Research, 14,3–23.

O’Reilly, C. (1989) Corporations, Culture, and Com-mitment: Motivation and Social Control inOrganizations. California Management Review, 31,9–25.

Ouchi, W.G. (1977) The Relationship betweenOrganizational Structure and OrganizationalControl. Administrative Science Quarterly, 22,95–113.

Ouchi, W.G. (1979) A Conceptual Framework forthe Design of Organizational Control Mecha-nisms. Management Science, 25, 833–48.

Ouchi, W.G. and Maguire, M.A. (1975)Organizational Control: Two Functions. Adminis-trative Science Quarterly, 20, 559–69.

Page, A.L. and Schirr, G.R. (2008) Growth andDevelopment of a Body of Knowledge: 16 Yearsof New Product Development Research, 1989–2004. Journal of Product Innovation Management,25, 233–48.

Paré, G. (2002) Implementing Clinical InformationSystems: A Multiple-Case Study within a USHospital. Health Services Management Research, 15,71–92.

Parker, V.A., Wubbenhorst, W.H., Young, G.J.,Desai, K.R. and Charns, M.P. (1999) Implement-ing Quality Improvement in Hospitals: The Roleof Leadership and Culture. American Journal ofMedical Quality, 14, 64–9.

Petter, S., Straub, D. and Rai, A. (2007) SpecifyingFormative Constructs in Information SystemsResearch. MIS Quarterly, 31, 623–56.

Piening, E.P. (2011) Insights into the ProcessDynamics of Innovation Implementation – TheCase of Public Hospitals in Germany. Public Man-agement Review, 13, 127–57.

Ringle, C.M., Wende, S. and Will, A. (2005)SmartPLS 2.0. SmartPLS, Hamburg (available atwww.smartpls.de).

Rosenau, P.V. and Linder, S.H. (2003) Two Decadesof Research Comparing For-Profit and NonprofitHealth Provider Performance in the UnitedStates. Social Science Quarterly, 84, 219–41.

Rothaermel, F.T. and Boeker, W. (2008) Old Technol-ogy Meets New Technology: Complementarities,Similarities, and Alliance Formation. StrategicManagement Journal, 29, 47–77.

Salge, T.O. and Vera, A. (2009) HospitalInnovativeness and Organizational Performance:Evidence from English Public Acute Care. HealthCare Management Review, 34, 54–67.

Salomo, S., Weise, J. and Gemünden, H.G. (2007)NPD Planning Activities and Innovation Perfor-mance: The Mediating Role of Process Manage-ment and the Moderating Effect of ProductInnovativeness. Journal of Product Innovation Man-agement, 24, 285–302.

Savitz, L.A. and Kaluzny, A.D. (2000) Assessing theImplementation of Clinical Process Innovations:A Cross-Case Comparison. Journal of HealthcareManagement, 45, 366–79.

Schultz, C., Zippel-Schultz, B. and Salomo, S. (2012)Hospital Innovation Portfolios: Key Determinants



of Size and Innovativeness. Health Care Manage-ment Review, 37, 132–43.

Schultz, C., Salomo, S., de Brentani, U. andKleinschmidt, E.J. (2013) How Formal ControlInfluences Decision-Making Clarity and Innova-tion Performance. Journal of Product InnovationManagement, 30, 430–47.

Sethi, R. and Iqbal, Z. (2008) Stage-Gate Controls,Learning Failure, and Adverse Effect on NovelNew Products. Journal of Marketing, 72, 118–34.

Sheremata, W.A. (2000) Centrifugal and CentripetalForces in Radical New Product Developmentunder Time Pressure. Academy of ManagementReview, 25, 389–408.

Shortell, S.M., O’Brien, J.L., Carman, J.M., Foster,R.W., Hughes, E.F., Boerstler, H. and O’Connor,E.J. (1995) Assessing the Impact of ContinuousQuality Improvement/Total Quality Manage-ment: Concept Versus Implementation. HealthServices Research, 30, 377–401.

Snell, S.A. (1992) Control Theory in StrategicHuman Resource Management: The MediatingEffect of Administrative Information. Academy ofManagement Journal, 35, 292–327.

Song, L.Z., Song, M. and Di Benedetto, C.A. (2009)A Staged Service Innovation Model. DecisionScience, 40, 571–99.

Tatikonda, M.V. and Montoya-Weiss, M.M. (2001)Integrating Operations and Marketing Perspec-tives of Product Innovation: The Influence ofOrganizational Process Factors and Capabilitieson Development Performance. ManagementScience, 47, 151–72.

Tiemann, O. and Schreyögg, J. (2009) Effects ofOwnership on Hospital Efficiency in Germany.BuR – Business Research, 2, 115–45.

Turner, K.L. and Makhija, M.V. (2006) The Role ofOrganizational Controls in Managing Knowl-edge. Academy of Management Review, 31, 197–217.

Ussahawanitchakit, P. (2011) CorporateProactiveness, Business Experience, Environmen-tal Complexity, and Firm Sustainability: Evidencefrom Information Technology Business in Thai-land. Journal of International Business & Economics,11, 66–74.

Venkatraman, N. (1989) Strategic Orientation ofBusiness Enterprises: The Construct, Dimension-ality, and Measurement. Management Science, 35,942–62.

Walston, S.L., Bogue, R.J. and Schwartz, M.J. (1999)The Effects of Reegineering: Fad or CompetitiveFactor?/Practitioner Application. Journal ofHealthcare Management, 44, 456–74; discussion474–6.

Walston, S.L., Burns, L.R. and Kimberly, J.R. (2000)Does Reengineering Really Work? An Examina-tion of the Context and Outcomes of HospitalReengineering Initiatives. Health ServicesResearch, 34, 1363–88.

Wassmer, U., Dussauge, P. and Planellas, M. (2010)How to Manage Alliances Better Than One at aTime. MIT Sloan Management Review, 51, 77–84.

Weiner, B.J., Shortell, S.M. and Alexander, J. (1997)Promoting Clinical Involvement in HospitalQuality Improvement Efforts: The Effects of TopManagement, Board, and Physician Leadership.Health Services Research, 32, 491–510.

Wheeler, J.R., Burkhardt, J., Alexander, J.A. andMagnus, S.A. (1999) Financial and OrganizationalDeterminants of Hospital Diversification intoSubacute Care. Health Services Research, 34, 61–81.

Wold, H.O.A. (1982) Soft Modeling: The BasicDesign and Some Extensions. In Jöreskorg, K.G.and Wold, H.O.A. (eds.), Systems Under IndirectObservation, North-Holland, Amsterdam, pp.1–54.

Wolfe, R.A. (1994) Organizational Innovation –Review, Critique and Suggested Research Direc-tions. Journal of Management Studies, 31, 405–31.

Zippel-Schultz, B. and Schultz, C. (2011) Mediatedand Moderated Effects of Business and ProjectPlanning on Innovation Projects in Hospitals.Creativity and Innovation Management, 20, 296–310.

Giso Labitzke ([email protected]) is anexternal PhD candidate at the Berlin Insti-tute of Technology. He studied businessadministration at Technische UniversitätBergakademie Freiberg and at the Univer-sity of Queensland in Brisbane, Australiaand holds a Diploma of Business Adminis-tration from Technische Universität Ber-gakademie Freiberg. He has worked as aconsultant at The Boston Consulting GroupGmbH since 2008. He primarily works onhealth care-related projects and is amember of the health care core group. Hisresearch focus is innovation management inhealth care.

Sven Svoboda ([email protected]) studied business studies at theBerlin Institute of Technology and theAarhus School of Business in Denmark andworked as a research assistant at the BerlinInstitute of Technology from December2010 to December 2011. He has worked as aresearch assistant at Kiel University sinceJanuary 2012. The focus of his research isinnovation management in health care.

Carsten Schultz ([email protected]) is Professor for Technology Manage-ment at Kiel University. His research con-centrates on service innovations with afocus on innovation management in healthcare and the management of service net-works. Further research interests includethe management of radical innovations anduniversity industry collaborations. He haspublished several books and articles in aca-demic journals.



the role of dedicated innovation functions for innovation process control and performance - an...

Documents